Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

Abstract

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery ( r = 0.65), maternal age ( r = 0.59), gravidity ( r = 0.56), and BMI ( r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

“…Preterm birth remains the largest cause of death amongst infants. While there may be biological risk factors in an appreciably large percentage of pregnancies that result in prematurity, behavioral components, such as sleep and physical activity habits, may also increase the risk of preterm birth 20 . We sought to demonstrate that inexpensive monitoring of pregnancy using wearables can signal deviations from physical activity and sleep patterns that are associated with full-term births with no complications.…”

“…Despite several biological studies, it has been hard to change the landscape of preterm birth from a global health perspective 20,21 . Studies of preterm birth that involve biological samples are expensive to conduct and their scale is limited.…”

Data-driven insights can transform women’s reproductive health

“…Integrating and analyzing different types of -omics data to study women’s health conditions can provide revelations in causes of disease and targets for treatment 59 . Multi-omics approaches have resulted in greater insights into biological signals associated with term and preterm birth 60 , 61 and could be increasingly leveraged to better understand pregnancy and other women’s health conditions. Moreover, digital twins can provide a data-driven way of monitoring, modeling, and managing conditions that can be tailored to an individual’s specific needs by integrating real-time data from various sources (e.g., clinical records, sensors, mobile health tracking applications, wearable devices) and artificial intelligence 62 .…”

Challenges to the study of gender dysphoria

“…Our work with preterm infants led us to recognise that gender dysphoria might have some of its origins in events occurring early on in development, perhaps even prior to birth. We have used novel machine‐learning algorithms integrating diverse features, such as multiomics, sociodemographic, psychosocial and environmental factors to predict spontaneous preterm birth and preeclampsia 3,4 . These latter two distinct adverse outcomes of pregnancy will hopefully be reduced both by the early prediction of preterm birth and preeclampsia months before they are likely to occur and by earlier interventions to prevent or mitigate the occurrences.…”